1. Part 5 - Chapter 17

2. Part 5 CURVE FITTING
Describes techniques to fit curves (curve fitting) to discrete data to obtain intermediate estimates.
There are two general approaches two curve fitting:
Data exhibit a significant degree of scatter. The strategy is to derive a single curve that represents the general trend of the data.
Data is very precise. The strategy is to pass a curve or a series of curves through each of the points.
In engineering two types of applications are encountered:
Trend analysis. Predicting values of dependent variable, may include extrapolation beyond data points or interpolation between data points.
Hypothesis testing. Comparing existing mathematical model with measured data.

3. Figure PT5.1

4. Mathematical Background
Simple Statistics/
In course of engineering study, if several measurements are made of a particular quantity, additional insight can be gained by summarizing the data in one or more well chosen statistics that convey as much information as possible about specific characteristics of the data set.
These descriptive statistics are most often selected to represent
The location of the center of the distribution of the data,
The degree of spread of the data.

5. Arithmetic mean. The sum of the individual data points (yi) divided by the number of points (n).
Standard deviation. The most common measure of a spread for a sample. or

6. Variance. Representation of spread by the square of the standard deviation.
Coefficient of variation. Has the utility to quantify the spread of data.
Degrees of freedom

7. Figure PT5.2

8. Figure PT5.3

9. Figure PT5.6

10. Least Squares Regression Chapter 17
Linear Regression
Fitting a straight line to a set of paired observations: (x1, y1), (x2, y2),…,(xn, yn).
y=a0+a1x+e
a1- slope
a0- intercept
e- error, or residual, between the model and the observations

11. Criteria for a “Best” Fit/
Minimize the sum of the residual errors for all available data:
n = total number of points
However, this is an inadequate criterion, so is the sum of the absolute values

12. Figure 17.2

13. Best strategy is to minimize the sum of the squares of the residuals between the measured y and the y calculated with the linear model:
Yields a unique line for a given set of data.

14. List-Squares Fit of a Straight Line/
Normal equations, can be solved simultaneously
Mean values

15. Figure 17.3

16. Figure 17.4

17. Figure 17.5

18. Sum of the squares of residuals around the regression line is Sr
“Goodness” of our fit/ If
Total sum of the squares around the mean for the dependent variable, y, is St
Sum of the squares of residuals around the regression line is Sr
St-Sr quantifies the improvement or error reduction due to describing data in terms of a straight line rather than as an average value.
r2-coefficient of determination
Sqrt(r2) – correlation coefficient

19. For a perfect fit
Sr=0 and r=r2=1, signifying that the line explains 100 percent of the variability of the data.
For r=r2=0, Sr=St, the fit represents no improvement.

20. Polynomial Regression
Some engineering data is poorly represented by a straight line. For these cases a curve is better suited to fit the data. The least squares method can readily be extended to fit the data to higher order polynomials (Sec. 17.2).

21. General Linear Least Squares
Minimized by taking its partial derivative w.r.t. each of the coefficients and setting the resulting equation equal to zero